Abstract
Texture analysis is important in several image segmentation and classification problems. Different image textures manifest themselves by dissimilarity in both the property values and the spatial interrelationships of their component texture primitives. We use this fact in a texture discrimination system. This paper focuses on how to apply texture operators based on co-occurrence matrix, texture filters and fractal dimension to the problem of object recognition and image segmentation.
Highlights
Texture analysis is important in several image segmentation and classification problems
This paper focuses on how to apply texture operators based on co-occurrence matrix, texture filters and fractal dimension to the problem of object recognition and image segmentation
Unsupervised image segmentation is a fundamental issue in image analysis and computer vision
Summary
Unsupervised image segmentation is a fundamental issue in image analysis and computer vision. The purpose of segmentation is to partition the image into regions of similar attribute like luminance, color or texture. Texture plays an important role in numerous computer vision applications, particularity in segmentation of images. Many useful properties for image description and interpretation are gained through texture observation and analysis. Texture classification categories are based sometimes on distinguishing feature as shown in [1] [2]. Markov Random Fields (MRF) defines a class of statistical models which enable to describe both the local and global properties of texture. The purpose of statistical methods is to characterize the stochastic properties in the spatial distribution of the gray level in the image
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